code: def __getitem__(self, i):
      example = dict((k, self.labels[k][i]) for k in self.labels)
      image = Image.open(example["file_path_"])
      if not image.mode == "RGB":
          image = image.convert("RGB")
  
      # default to score-sde preprocessing
      img = np.array(image).astype(np.uint8)
      crop = min(img.shape[0], img.shape[1])
      (
          h,
          w,
      ) = (
          img.shape[0],
          img.shape[1],
      )
      img = img[
          (h - crop) // 2 : (h + crop) // 2,
          (w - crop) // 2 : (w + crop) // 2,
      ]
  
      image = Image.fromarray(img)
      if self.size is not None:
          image = image.resize(
              (self.size, self.size), resample=self.interpolation
          )
  
      image = self.flip(image)
      image = np.array(image).astype(np.uint8)
      example["image"] = (image / 127.5 - 1.0).astype(np.float32)
      return example
  
imports: import os
  import numpy
  import PIL
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
label_desc: Use image node_id 3 for calling the LSUNBase obj's underlying member method code with example usage: obj.__getitem__(i) and returns: example